This study seeks to explore the potential of data-driven methods for developing a financial statement fraud prediction model. We emphasize that building a fraud prediction model that can be used to detect fraud in real-world applications should receive attention from researchers. However, the severe class imbalance issue and the complex nature of fraudulent activities make it a rather challenging task. To address these problems, we apply the combinations of different sampling techniques and tree-based ensemble classifiers to an extensive set of raw financial statement data. The results show that the models using an extensive set of raw financial data, undersampling techniques and boosting tree classifiers are superior in fraud detection. Moreover, several features without a priori knowledge are identified to be important for fraud prediction models by feature importance evaluation. Accordingly, this study provides a methodological guide for designing fraud prediction models for real-world applications and serves as a preliminary step of the knowledge discovery process to complement fraud detection knowledge systems.

中文翻译：

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使用数据驱动的方法检测真实场景中的财务报表欺诈


本研究旨在探索数据驱动方法开发财务报表欺诈预测模型的潜力。我们强调，建立可用于检测现实应用中欺诈的欺诈预测模型应该受到研究人员的关注。然而，严重的类别失衡问题和欺诈活动的复杂性使其成为一项相当具有挑战性的任务。为了解决这些问题，我们将不同采样技术和基于树的集成分类器的组合应用于大量原始财务报表数据。结果表明，使用大量原始金融数据、欠采样技术和增强树分类器的模型在欺诈检测方面表现出色。此外，通过特征重要性评估，一些没有先验知识的特征被识别为对于欺诈预测模型很重要。因此，本研究为设计实际应用的欺诈预测模型提供了方法指南，并作为知识发现过程的第一步，以补充欺诈检测知识系统。